Purpose: Isolating the mainlobe and sidelobe contribution to the ultrasound image can improve imaging contrast by removing off-axis clutter. Previous work achieves this separation of mainlobe and sidelobe contributions based on the covariance of received signals. However, the formation of a covariance matrix at each imaging point can be computationally burdensome and memory intensive for real-time applications. Our work demonstrates that the mainlobe and sidelobe contributions to the ultrasound image can be isolated based on the receive aperture spectrum, greatly reducing computational and memory requirements.
Approach: The separation of mainlobe and sidelobe contributions to the ultrasound image is shown in simulation, in vitro, and in vivo using the aperture spectrum method and multicovariate imaging of subresolution targets (MIST). Contrast, contrast-to-noise-ratio (CNR), and speckle signal-to-noise-ratio are used to compare the aperture spectrum approach with MIST and conventional delay-and-sum (DAS) beamforming.
Results: The aperture spectrum approach improves contrast by 1.9 to 6.4 dB beyond MIST and 8.9 to 13.5 dB beyond conventional DAS B-mode imaging. However, the aperture spectrum approach yields speckle texture similar to DAS. As a result, the aperture spectrum-based approach has less CNR than MIST but greater CNR than conventional DAS. The CPU implementation of the aperture spectrum-based approach is shown to reduce computation time by a factor of 9 and memory consumption by a factor of 128 for a 128-element transducer.
Conclusions: The mainlobe contribution to the ultrasound image can be isolated based on the receive aperture spectrum, which greatly reduces the computational cost and memory requirement of this approach as compared with MIST.
Keywords: aperture spectrum; coherence factor; model-based image reconstruction; spatial covariance.
© 2022 Society of Photo-Optical Instrumentation Engineers (SPIE).